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Ruzgar ?????
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Update app.py
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app.py
CHANGED
@@ -1,151 +1,122 @@
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import
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# Artwork by @allison_horst
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ui.input_selectize(
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"xvar",
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"X variable",
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numeric_cols,
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selected="Bill Length (mm)",
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),
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ui.input_selectize(
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"yvar",
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"Y variable",
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numeric_cols,
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selected="Bill Depth (mm)",
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),
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ui.input_checkbox_group(
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"species", "Filter by species", species, selected=species
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),
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ui.hr(),
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ui.input_switch("by_species", "Show species", value=True),
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ui.input_switch("show_margins", "Show marginal plots", value=True),
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),
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ui.output_ui("value_boxes"),
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ui.output_plot("scatter", fill=True),
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ui.help_text(
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"Artwork by ",
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ui.a("@allison_horst", href="https://twitter.com/allison_horst"),
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class_="text-end",
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),
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),
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)
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def server(input: Inputs, output: Outputs, session: Session):
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@reactive.Calc
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def filtered_df() -> pd.DataFrame:
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"""Returns a Pandas data frame that includes only the desired rows"""
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# This calculation "req"uires that at least one species is selected
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req(len(input.species()) > 0)
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# Filter the rows so we only include the desired species
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return df[df["Species"].isin(input.species())]
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@output
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@render.plot
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def scatter():
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"""Generates a plot for Shiny to display to the user"""
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# The plotting function to use depends on whether margins are desired
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plotfunc = sns.jointplot if input.show_margins() else sns.scatterplot
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plotfunc(
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data=filtered_df(),
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x=input.xvar(),
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y=input.yvar(),
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palette=palette,
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hue="Species" if input.by_species() else None,
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hue_order=species,
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legend=False,
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)
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@output
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@render.ui
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def value_boxes():
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df = filtered_df()
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def penguin_value_box(title: str, count: int, bgcol: str, showcase_img: str):
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return ui.value_box(
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title,
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count,
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{"class_": "pt-1 pb-0"},
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showcase=ui.fill.as_fill_item(
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ui.tags.img(
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{"style": "object-fit:contain;"},
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src=showcase_img,
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)
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),
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theme_color=None,
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style=f"background-color: {bgcol};",
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)
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if not input.by_species():
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return penguin_value_box(
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"Penguins",
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len(df.index),
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bg_palette["default"],
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# Artwork by @allison_horst
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showcase_img="penguins.png",
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)
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value_boxes = [
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penguin_value_box(
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name,
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len(df[df["Species"] == name]),
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bg_palette[name],
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# Artwork by @allison_horst
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showcase_img=f"{name}.png",
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)
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for name in species
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# Only include boxes for _selected_ species
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if name in input.species()
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]
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return ui.layout_column_wrap(*value_boxes, width = 1 / len(value_boxes))
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# "darkorange", "purple", "cyan4"
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colors = [[255, 140, 0], [160, 32, 240], [0, 139, 139]]
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colors = [(r / 255.0, g / 255.0, b / 255.0) for r, g, b in colors]
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palette: Dict[str, Tuple[float, float, float]] = {
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"Adelie": colors[0],
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"Chinstrap": colors[1],
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"Gentoo": colors[2],
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"default": sns.color_palette()[0], # type: ignore
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}
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)
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import os
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from flask import Flask, request, jsonify
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from dotenv import load_dotenv
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from pymongo import MongoClient
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import openai
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from datetime import datetime
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load_dotenv()
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app = Flask(__name__)
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# MongoDB bağlantısı
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client = MongoClient(os.getenv("MONGODB_URI"))
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db = client[os.getenv("MONGODB_DB_NAME")]
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# OpenAI API anahtarı
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openai.api_key = os.getenv("OPENAI_API_KEY")
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# Model grupları ve modeller
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MODEL_GROUPS = {
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"GPT4_GROUP": ["gpt-4", "gpt-4-0125-preview", "gpt-4-0613", "gpt-4-1106-preview"],
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"GPT4_TURBO_GROUP": ["gpt-4-turbo", "gpt-4-turbo-preview"],
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"GPT35_GROUP": ["gpt-3.5-turbo", "gpt-3.5-turbo-0125", "gpt-3.5-turbo-16k", "gpt-3.5-turbo-1106"],
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}
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MODELS = {
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"GPT4": MODEL_GROUPS["GPT4_GROUP"] + MODEL_GROUPS["GPT4_TURBO_GROUP"],
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"GPT35": MODEL_GROUPS["GPT35_GROUP"],
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"IMAGE": ["dall-e-2", "dall-e-3"],
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}
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# Kullanıcı seviyeleri
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TIERS = {
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1: {
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"models": MODELS["GPT35"],
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"limits": {"daily": 100, "GPT35_GROUP": 100}
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},
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2: {
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"models": MODELS["GPT35"] + MODELS["GPT4"],
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"limits": {"daily": 500, "GPT35_GROUP": 300, "GPT4_GROUP": 150, "GPT4_TURBO_GROUP": 50}
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},
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3: {
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"models": MODELS["GPT35"] + MODELS["GPT4"] + MODELS["IMAGE"],
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"limits": {"daily": 1000, "GPT35_GROUP": 500, "GPT4_GROUP": 300, "GPT4_TURBO_GROUP": 100, "dall-e-2": 50, "dall-e-3": 25}
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}
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}
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def get_user_tier(api_key):
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user = db.users.find_one({"api_key": api_key})
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if user:
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return user["tier"]
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for i in range(1, 4):
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if os.getenv(f"USER_{i}_API_KEY") == api_key:
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return int(os.getenv(f"USER_{i}_TIER"))
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return None
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def get_model_group(model):
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for group, models in MODEL_GROUPS.items():
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if model in models:
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return group
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return model
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def check_rate_limit(api_key, tier, model):
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now = datetime.now()
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today = now.date().isoformat()
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model_group = get_model_group(model)
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usage = db.usage.find_one_and_update(
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{"api_key": api_key, "date": today},
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{"$inc": {"daily": 1, model_group: 1}},
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upsert=True,
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return_document=True
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)
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tier_limits = TIERS[tier]["limits"]
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if usage["daily"] > tier_limits["daily"]:
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raise Exception("Daily rate limit exceeded")
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if usage.get(model_group, 0) > tier_limits.get(model_group, float("inf")):
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raise Exception(f"Rate limit for {model_group} exceeded")
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@app.route('/v1/chat/completions', methods=['POST'])
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def chat_completions():
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data = request.json
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api_key = request.headers.get('Authorization', '').replace('Bearer ', '')
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user_tier = get_user_tier(api_key)
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if not user_tier:
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return jsonify({"error": "Invalid API key"}), 401
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model = data.get('model')
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if model not in TIERS[user_tier]["models"]:
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return jsonify({"error": "Model not available for your tier"}), 403
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try:
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check_rate_limit(api_key, user_tier, model)
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response = openai.ChatCompletion.create(**data)
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return jsonify(response)
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except Exception as e:
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return jsonify({"error": str(e)}), 429
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@app.route('/v1/images/generations', methods=['POST'])
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def image_generations():
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data = request.json
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api_key = request.headers.get('Authorization', '').replace('Bearer ', '')
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user_tier = get_user_tier(api_key)
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if not user_tier:
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return jsonify({"error": "Invalid API key"}), 401
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model = data.get('model', 'dall-e-2')
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if model not in TIERS[user_tier]["models"]:
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return jsonify({"error": "Model not available for your tier"}), 403
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try:
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check_rate_limit(api_key, user_tier, model)
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response = openai.Image.create(**data)
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return jsonify(response)
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except Exception as e:
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return jsonify({"error": str(e)}), 429
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if __name__ == '__main__':
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app.run(host='0.0.0.0', port=7860)
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